Evidence map›Paper›PMID 39965013›Full record

ArticlePloS one2025

Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms.

Juntu Li, Linfeng Tao, Yanyou Zhou, Yue Zhu, Chao Li, Yiyuan Pan, Ping Yao, Xuefeng Qian, Jun Liu

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Juntu LiDepartment of Critical Care Medicine and Emergency, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital), Gusu School, Nanjing Medical University, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, Jiangsu, China.ORCID https://orcid.org/0009-0001-7571-613X
Linfeng TaoDepartment of Critical Care Medicine and Emergency, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital), Gusu School, Nanjing Medical University, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, Jiangsu, China.
Yanyou ZhouDepartment of Critical Care Medicine and Emergency, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital), Gusu School, Nanjing Medical University, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, Jiangsu, China.
Yue ZhuDepartment of Breast and Thyroid Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital), Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
Chao LiDepartment of Critical Care Medicine and Emergency, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital), Gusu School, Nanjing Medical University, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, Jiangsu, China.
Yiyuan PanDepartment of Critical Care Medicine and Emergency, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital), Gusu School, Nanjing Medical University, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, Jiangsu, China.
Ping YaoDepartment of Critical Care Medicine and Emergency, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital), Gusu School, Nanjing Medical University, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, Jiangsu, China.
Xuefeng QianDepartment of Critical Care Medicine and Emergency, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital), Gusu School, Nanjing Medical University, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, Jiangsu, China.
Jun LiuDepartment of Critical Care Medicine and Emergency, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital), Gusu School, Nanjing Medical University, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, Jiangsu, China.ORCID https://orcid.org/0000-0002-3680-8998

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer's disease (AD) and COVID-19. However, there is little research into the mechanisms underlying these phenomena. Therefore, we conducted this study to identify key genes in COVID-19 associated with AD, and evaluate their correlation with immune cells characteristics and metabolic pathways.

methodsTranscriptome analyses were used to identify common biomolecular markers of AD and COVID-19. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on gene chip datasets (GSE213313, GSE5281, and GSE63060) from AD and COVID-19 patients to identify genes associated with both conditions. Gene ontology (GO) enrichment analysis identified common molecular mechanisms. The core genes were identified using machine learning. Subsequently, we evaluated the relationship between these core genes and immune cells and metabolic pathways. Finally, our findings were validated through single-cell analysis.

resultsThe study identified 484 common differentially expressed genes (DEGs) by taking the intersection of genes between AD and COVID-19. The black module, containing 132 genes, showed the highest association between the two diseases according to WGCNA. GO enrichment analysis revealed that these genes mainly affect inflammation, cytokines, immune-related functions, and signaling pathways related to metal ions. Additionally, a machine learning approach identified eight core genes. We identified links between these genes and immune cells and also found a association between EIF3H and oxidative phosphorylation.

conclusionThis study identifies shared genes, pathways, immune alterations, and metabolic changes potentially contributing to the pathogenesis of both COVID-19 and AD.

Indexed as

Alzheimer DiseaseComputational BiologyCOVID-19Machine LearningAlgorithmsBiomarkersGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansMetabolic Networks and PathwaysSARS-CoV-2Single-Cell AnalysisTranscriptomeBiomarkers

Identifiers

PMID39965013
PMCPMC11835241

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.